Deployment Ops

Organization

VectorSpaceLab

deployment-ops

Guides CI/CD setup, deployment commands, observability, data ingestion, and Gemini Enterprise registration for Agent Starter Pack projects.

Overview

Publisher	VectorSpaceLab
Repository	`AREX-Skill`
Skill name	`deployment-ops`
Stars	341
Forks	26
Bundled files	7
License	Apache-2.0
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
7 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by VectorSpaceLab on GitHub. Read the source before you install it.

Installation

Install the Deployment Ops AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/agent-starter-pack/sub-skills/deployment-ops
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/agent-starter-pack/sub-skills/deployment-ops

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/VectorSpaceLab/AREX-Skill.git /tmp/AREX-Skill
mkdir -p .claude/skills
cp -r /tmp/AREX-Skill/skills/repositories/repo-skills/agent-starter-pack/sub-skills/deployment-ops .claude/skills/deployment-ops

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Deployment Ops in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Deployment Ops on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Deployment Ops is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Deployment operations

Use this sub-skill when the user is turning an Agent Starter Pack project into a deployable, observable, or registered cloud asset.

Covers

setup-cicd and its Terraform/runner setup.
Generated-project deployment commands such as make setup-dev-env, make setup-datastore, make deploy, make data-ingestion, make sync-data, make register-gemini-enterprise, make inspector, and the related local-development targets.
Data ingestion, observability, and Gemini Enterprise registration guidance.
Cloud-provider prerequisites such as GitHub CLI, gcloud, Terraform, repository ownership, and generated deployment metadata.

Excludes

New-project template selection belongs in project-scaffolding.
In-place project edits and upgrades belong in project-maintenance.

Read first

../../references/cli-reference.md for command routing.
../../references/package-overview.md for install and sanity-check guidance.
references/workflows.md for the end-to-end deployment flow.
references/ci-cd-reference.md for setup-cicd details.
references/generated-project-commands.md for the generated Makefile command map.
references/data-ingestion.md for RAG and datastore setup.
references/observability.md for telemetry and BigQuery analytics.
references/gemini-enterprise.md for registration flows.
references/troubleshooting.md for cloud and auth failures.

Common workflow

Confirm whether the user is asking about a generated project, not the source package itself.
Decide whether the task is CI/CD bootstrap, runtime deployment, data ingestion, observability, or Gemini Enterprise registration.
Check the relevant generated-project command or cloud prerequisite before suggesting a fix.
Use the sub-reference pages to explain the concrete command sequence and likely failure points.
Route project-generation questions back to project-scaffolding and project-maintenance questions back to project-maintenance.

Useful signals

setup-cicd, Terraform, Cloud Build, GitHub Actions, gcloud, gh, local-state
make deploy, make setup-dev-env, make data-ingestion, make sync-data, make inspector, make register-gemini-enterprise
agent_engine, cloud_run, gke, deployment_metadata.json, agent card, Gemini Enterprise
vertex_ai_search, vertex_ai_vector_search, telemetry, Cloud Trace, BigQuery analytics, observability

Validation mindset

Do not claim cloud readiness from package importability alone.
Distinguish local command discovery from live cloud provisioning.
Keep destructive cleanup, live-deployment, and credentialed operations out of the default path unless the user explicitly asks for them.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Deployment Ops AI skill do?

Guides CI/CD setup, deployment commands, observability, data ingestion, and Gemini Enterprise registration for Agent Starter Pack projects.

Why use Deployment Ops on TypingMind?

Because you install it once and use it with any model. Deployment Ops is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Deployment Ops in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/VectorSpaceLab/AREX-Skill/tree/main/skills/repositories/repo-skills/agent-starter-pack/sub-skills/deployment-ops. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Deployment Ops?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Deployment Ops?

As many as you like. As long as a model supports skills, you can use Deployment Ops with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Deployment Ops AI skill free?

Yes. It is published on GitHub by VectorSpaceLab under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Deployment operations

Covers

Excludes

Read first

Common workflow

Useful signals

Validation mindset

Bundled files

Frequently asked questions

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